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 "metadata": {
  "name": "",
  "signature": "sha256:be7283219b5e8e0d35e4574b9d4cca6f42785c5f93ea9052e16ca472c5c761ed"
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This notebook contains examples related to survival analysis, based on Chapter 13 of<br>\n",
      "Think Stats, 2nd Edition<br>\n",
      "by Allen Downey<br>\n",
      "available from [thinkstats2.com](http://thinkstats2.com)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import print_function, division\n",
      "\n",
      "import nsfg\n",
      "import survival\n",
      "\n",
      "import thinkstats2\n",
      "import thinkplot\n",
      "\n",
      "import pandas\n",
      "import numpy\n",
      "from lifelines import KaplanMeierFitter\n",
      "from collections import defaultdict\n",
      "\n",
      "import matplotlib.pyplot as pyplot\n",
      "\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first example looks at pregnancy lengths for respondents in the National Survey of Family Growth (NSFG).  This is the easy case, because we can directly compute the CDF of pregnancy length; from that we can get the survival function:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "preg = nsfg.ReadFemPreg()\n",
      "complete = preg.query('outcome in [1, 3, 4]').prglngth"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cdf = thinkstats2.Cdf(complete, label='cdf')\n",
      "sf = survival.SurvivalFunction(cdf, label='survival')\n",
      "thinkplot.Plot(sf)\n",
      "thinkplot.Config(xlabel='duration (weeks)', ylabel='survival function')\n",
      "#thinkplot.Save(root='survival_talk1', formats=['png'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nzo+stfzwn95xsZrkpUAQkZTwkcvmELpJbfnqfax5R/ckjJQCQURSwsxpY7jg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\nIiKSMv4/Yt2qyoDaIv4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7c2b0a7f50>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "About 17% of pregnancies end in the first trimester, but a large majorty pregnancies that exceed 13 weeks go to full term.\n",
      "\n",
      "Next we can use the survival function to compute the hazard function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hf = sf.MakeHazard(label='hazard')\n",
      "thinkplot.Plot(hf)\n",
      "thinkplot.Config(xlabel='duration (weeks)', ylabel='hazard function', ylim=[0, 0.75], loc='upper left')\n",
      "#thinkplot.Save(root='survival_talk2', formats=['png'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KBqmtMrWERAL6bAEA0msI/rz0+rNUIlLySq3JCNKnr3A2G4WVQxARyawYkqy5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\nREREREREREREREREREREREREpGT8fx4Q6qPIkp+cAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7c2b062450>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The hazard function shows the same pattern: the lowest hazard in the second semester, and by far the highest hazard around 30 weeks.\n",
      "\n",
      "We can also use the survival curve to compute mean remaining lifetime as a function of how long the pregnancy has gone."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rem_life = sf.RemainingLifetime()\n",
      "thinkplot.Plot(rem_life)\n",
      "thinkplot.Config(xlabel='weeks', ylabel='mean remaining weeks', legend=False)\n",
      "#thinkplot.Save(root='survival_talk3', formats=['png'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3cIX78T+A1UCbj++Rzam3g/KBckx10rfQ7SARwGxif++aIg7Xe+sN/VteKv8+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\nIiIiIiIiIiIiIiJh7f8DhprUytXj0YAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7c2a243090>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For 38 weeks, the finish line approaches nearly linearly.  But at 39 weeks, the expected remaining time levels off abruptly.  After that, each week that passes brings the finish line no closer.\n",
      "\n",
      "I started with pregnancy lengths because they represent the easy case where the distribution of lifetimes is known.  But often in observational studies we have a combination of complete cases, where the lifetime is known, and ongoing cases where we have a lower bound on the lifetime.\n",
      "\n",
      "As an example, we'll look at the time until first marriage for women in the NSFG."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resp = survival.ReadFemResp2002()\n",
      "len(resp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "7643"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For complete cases, we know the respondent's age at first marriage.  For ongoing cases, we have the respondent's age when interviewed."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "complete = resp[resp.evrmarry == 1].agemarry\n",
      "ongoing = resp[resp.evrmarry == 0].age"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are only a few cases with unknown marriage dates."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nan = complete[numpy.isnan(complete)]\n",
      "len(nan)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "37"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "EstimateHazardFunction is an implementation of Kaplan-Meier estimation.\n",
      "\n",
      "With an estimated hazard function, we can compute a survival function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hf = survival.EstimateHazardFunction(complete, ongoing)\n",
      "sf = hf.MakeSurvival()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here's the hazard function:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "thinkplot.Plot(hf)\n",
      "thinkplot.Config(xlabel='age (years)', ylabel='hazard function', legend=False)\n",
      "#thinkplot.Save(root='survival_talk4', formats=['png'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tY+P8+uhJAP78glWsaGu27OOkrUwnEjS4NBfya+IpRR6LX767xwhLPjUT47fHRrmit9vf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\nBEGobf4/7rNKSuRxGRkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7c2b0622d0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As expected, the hazard function is highest in the mid-20s.  The function increases again after 35, but that is an artifact of the estimation process and a misleading visualization.  Making a better representation of the hazard function is on my TODO list.\n",
      "\n",
      "Here's the survival function:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "thinkplot.Plot(sf)\n",
      "thinkplot.Config(xlabel='age (years)',\n",
      "               ylabel='prob unmarried',\n",
      "               ylim=[0, 1],\n",
      "               legend=False)\n",
      "#thinkplot.Save(root='survival_talk5', formats=['png'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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n5/XUJSIFxGNZlPuLKPcX0YxJiSON2wkn4kxEgszGogTjUebiUeZiMYaC00SS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\n4ob/D4b/LpLIkSu2AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7c04864a50>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The survival function naturally smooths out the noisiness in the hazard function.\n",
      "\n",
      "With the survival curve, we can also compute the probability of getting married before age 44, as a function of current age."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ss = sf.ss\n",
      "end_ss = ss[-1]\n",
      "prob_marry44 = (ss - end_ss) / ss\n",
      "thinkplot.Plot(sf.ts, prob_marry44)\n",
      "thinkplot.Config(xlabel='age (years)', ylabel='prob marry before 44', ylim=[0, 1], legend=False)\n",
      "#thinkplot.Save(root='survival_talk6', formats=['png'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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98QN0hHs8iGzzKSGIiKyjbJe4lp9hNjuxKjFYWBzpuo94G9QWlBBERJpk2zap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\nFRQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7c2af8c250>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After age 20, the probability of getting married drops off nearly linearly.\n",
      "\n",
      "We can also compute the median time until first marriage as a function of age."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "func = lambda pmf: pmf.Percentile(50)\n",
      "rem_life = sf.RemainingLifetime(filler=numpy.inf, func=func)\n",
      "thinkplot.Plot(rem_life)\n",
      "thinkplot.Config(ylim=[0, 15],\n",
      "                 xlim=[11, 31],\n",
      "                 xlabel='age (years)',\n",
      "                 ylabel='median remaining years')\n",
      "#thinkplot.Save(root='survival_talk7', formats=['png'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wJN8+oFhEREpeIczZVwDdgWkBxyEiUrLKA/79LYAXgAHYCH+bysrKbbcjkQiR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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7c2955b850>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At age 11, young women are a median of 14 years away from their first marriage.  At age 23, the median has fallen to 7 years.  But an never-married woman at 30 is back to a median remaining time of 14 years.\n",
      "\n",
      "I also want to demonstrate `lifelines`, which is a Python module that provides Kaplan-Meier estimation and other tools related to survival analysis.\n",
      "\n",
      "To use lifelines, we have to get the data into a different format.  First I'll add a column to the respondent DataFrame with \"event times\", meaning either age at first marriage OR age at time of interview."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resp['event_times'] = resp.age\n",
      "resp['event_times'][resp.evrmarry == 1] = resp.agemarry\n",
      "len(resp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "7643"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lifelines doesn't like NaNs, so let's get rid of them:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cleaned = resp.dropna(subset=['event_times'])\n",
      "len(cleaned)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "7606"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can use the KaplanMeierFitter, passing the series of event times and a series of booleans indicating which events are complete and which are ongoing:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmf = KaplanMeierFitter()\n",
      "kmf.fit(cleaned.event_times, cleaned.evrmarry)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "<lifelines.KaplanMeierFitter: fitted with 7606 observations, 3517 censored>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here are the results from my implementation compared with the results from Lifelines."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "thinkplot.Plot(sf)\n",
      "thinkplot.Config(xlim=[0, 45], legend=False)\n",
      "pyplot.grid()\n",
      "kmf.survival_function_.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f7c2955ba90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f7c294a7ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xBqxd6zpNGd/aqYSvuYJQTV2khrvkEjtPzBlnwIIF0Lat60SSTKqpi2SIKVPs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\nIiIiIiIiIiIiIiIiIiIiIiKSMv8fAjo/+ACOVQwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7c2b290f10>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "They are at least visually similar.  Just to double check, I ran a small example:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "complete = [1, 2, 3]\n",
      "ongoing = [2.5, 3.5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here's the hazard function:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hf = survival.EstimateHazardFunction(complete, ongoing)\n",
      "hf.series"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "1    0.20\n",
        "2    0.25\n",
        "3    0.50\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And the survival function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sf = hf.MakeSurvival()\n",
      "sf.ts, sf.ss"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "(array([1, 2, 3]), array([ 0.8,  0.6,  0.3]))"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "My implementation only evaluate the survival function at times when a completed event occurred.\n",
      "\n",
      "Next I'll reformat the data for lifelines:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "T = pandas.Series(complete + ongoing)\n",
      "E = [1, 1, 1, 0, 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And run the KaplanMeier Fitter:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmf = KaplanMeierFitter()\n",
      "kmf.fit(T, E)\n",
      "kmf.survival_function_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>KM-estimate</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>timeline</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0.0</th>\n",
        "      <td> 1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1.0</th>\n",
        "      <td> 0.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2.0</th>\n",
        "      <td> 0.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2.5</th>\n",
        "      <td> 0.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3.0</th>\n",
        "      <td> 0.3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3.5</th>\n",
        "      <td> 0.3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "          KM-estimate\n",
        "timeline             \n",
        "0.0               1.0\n",
        "1.0               0.8\n",
        "2.0               0.6\n",
        "2.5               0.6\n",
        "3.0               0.3\n",
        "3.5               0.3"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The results are the same, except that the Lifelines implementation evaluates the survival function at all event times, complete or not.\n",
      "\n",
      "Next, I'll use additional data from the NSFG to investigate \"marriage curves\" for successive generations of women.\n",
      "\n",
      "Here's data from the last 4 cycles of the NSFG:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resp5 = survival.ReadFemResp1995()\n",
      "resp6 = survival.ReadFemResp2002()\n",
      "resp7 = survival.ReadFemResp2010()\n",
      "resp8 = survival.ReadFemResp2013()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This function takes a respondent DataFrame and estimates survival curves:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def EstimateSurvival(resp):\n",
      "    \"\"\"Estimates the survival curve.\n",
      "\n",
      "    resp: DataFrame of respondents\n",
      "\n",
      "    returns: pair of HazardFunction, SurvivalFunction\n",
      "    \"\"\"\n",
      "    complete = resp[resp.evrmarry == 1].agemarry\n",
      "    ongoing = resp[resp.evrmarry == 0].age\n",
      "\n",
      "    hf = survival.EstimateHazardFunction(complete, ongoing)\n",
      "    sf = hf.MakeSurvival()\n",
      "\n",
      "    return hf, sf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This function takes a list of respondent files, resamples them, groups by decade, optionally generates predictions, and returns a map from group name to a list of survival functions (each based on a different resampling):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def ResampleSurvivalByDecade(resps, iters=101, predict_flag=False, omit=[]):\n",
      "    \"\"\"Makes survival curves for resampled data.\n",
      "\n",
      "    resps: list of DataFrames\n",
      "    iters: number of resamples to plot\n",
      "    predict_flag: whether to also plot predictions\n",
      "    \n",
      "    returns: map from group name to list of survival functions\n",
      "    \"\"\"\n",
      "    sf_map = defaultdict(list)\n",
      "\n",
      "    # iters is the number of resampling runs to make\n",
      "    for i in range(iters):\n",
      "        \n",
      "        # we have to resample the data from each cycles separately\n",
      "        samples = [thinkstats2.ResampleRowsWeighted(resp) \n",
      "                   for resp in resps]\n",
      "        \n",
      "        # then join the cycles into one big sample\n",
      "        sample = pandas.concat(samples, ignore_index=True)\n",
      "        for decade in omit:\n",
      "            sample = sample[sample.decade != decade]\n",
      "        \n",
      "        # group by decade\n",
      "        grouped = sample.groupby('decade')\n",
      "\n",
      "        # and estimate (hf, sf) for each group\n",
      "        hf_map = grouped.apply(lambda group: EstimateSurvival(group))\n",
      "\n",
      "        if predict_flag:\n",
      "            MakePredictionsByDecade(hf_map)       \n",
      "\n",
      "        # extract the sf from each pair and acculumulate the results\n",
      "        for name, (hf, sf) in hf_map.iteritems():\n",
      "            sf_map[name].append(sf)\n",
      "             \n",
      "            \n",
      "    return sf_map"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And here's how the predictions work:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def MakePredictionsByDecade(hf_map, **options):\n",
      "    \"\"\"Extends a set of hazard functions and recomputes survival functions.\n",
      "\n",
      "    For each group in hf_map, we extend hf and recompute sf.\n",
      "\n",
      "    hf_map: map from group name to (HazardFunction, SurvivalFunction)\n",
      "    \"\"\"\n",
      "    # TODO: this only works if the names and values are in increasing order,\n",
      "    # which is true when hf_map is a GroupBy object, but not generally\n",
      "    # true for maps.\n",
      "    names = hf_map.index.values\n",
      "    hfs = [hf for (hf, sf) in hf_map.values]\n",
      "    \n",
      "    # extend each hazard function using data from the previous cohort,\n",
      "    # and update the survival function\n",
      "    for i, hf in enumerate(hfs):\n",
      "        if i > 0:\n",
      "            hf.Extend(hfs[i-1])\n",
      "        sf = hf.MakeSurvival()\n",
      "        hf_map[names[i]] = hf, sf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This function takes a list of survival functions and returns a confidence interval: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def MakeSurvivalCI(sf_seq, percents):\n",
      "    \n",
      "    # find the union of all ts where the sfs are evaluated\n",
      "    ts = set()\n",
      "    for sf in sf_seq:\n",
      "        ts |= set(sf.ts)\n",
      "    \n",
      "    ts = list(ts)\n",
      "    ts.sort()\n",
      "    \n",
      "    # evaluate each sf at all times\n",
      "    ss_seq = [sf.Probs(ts) for sf in sf_seq]\n",
      "    \n",
      "    # return the requested percentiles from each column\n",
      "    rows = thinkstats2.PercentileRows(ss_seq, percents)\n",
      "    return ts, rows\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make survival curves without predictions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resps = [resp5, resp6, resp7, resp8]\n",
      "sf_map = ResampleSurvivalByDecade(resps)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make survival curves with predictions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "resps = [resp5, resp6, resp7, resp8]\n",
      "sf_map_pred = ResampleSurvivalByDecade(resps, predict_flag=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This function plots survival curves:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def PlotSurvivalFunctionByDecade(sf_map, predict_flag=False):\n",
      "    thinkplot.PrePlot(len(sf_map))\n",
      "\n",
      "    for name, sf_seq in sorted(sf_map.iteritems(), reverse=True):\n",
      "        ts, rows = MakeSurvivalCI(sf_seq, [10, 50, 90])\n",
      "        thinkplot.FillBetween(ts, rows[0], rows[2], color='gray')\n",
      "        if predict_flag:\n",
      "            thinkplot.Plot(ts, rows[1], color='gray')\n",
      "        else:\n",
      "            thinkplot.Plot(ts, rows[1], label='%d0s'%name)\n",
      "\n",
      "    thinkplot.Config(xlabel='age(years)', ylabel='prob unmarried',\n",
      "                     xlim=[15, 45], ylim=[0, 1], legend=True, loc='upper right')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can plot results without predictions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PlotSurvivalFunctionByDecade(sf_map)\n",
      "#thinkplot.Save(root='survival_talk8', formats=['png'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1ttn4xVbuX16Nxx/GYDDw4ysm8ZWZhx+nEIRTiegyGvldRkKPBHM6dnMsAKFQ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\ny0YPN3pq2gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7c2b281490>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And plot again with predictions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PlotSurvivalFunctionByDecade(sf_map_pred, predict_flag=True)\n",
      "PlotSurvivalFunctionByDecade(sf_map)\n",
      "#thinkplot.Save(root='survival_talk9', formats=['png'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7xI7DPcnw4mIimRijen8ZB1oCsLmMim+VlZXouk5NTc1FrdYkEAguLkIgXABC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\nHIexVJL9Z45zdmSIUY+DswBzkY7CmmCMdU2ttETjNEWiNPiDdU1N1dN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       "text": [
        "<matplotlib.figure.Figure at 0x7f7c2aefda50>"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The gray regions show the confidence intervals for the estimates and predictions.\n",
      "\n",
      "Although the last two cohorts are lagging their predecessors, if we assume that their hazard function from here out will be similar to previous cohorts, they are on track to reach marriage rates at age 45 that are similar to previous cohorts.\n",
      "\n",
      "Also, at the risk of overinterpreting noise, it looks like the 90s cohort might have delayed marriage in the last few years and then made up for lost time, possibly as a reaction to an improving economy.  To investigate that conjecture, it would be useful to cut a different cross section of this data, with time on the x-axis, rather than age."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    }
   ],
   "metadata": {}
  }
 ]
}